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Communities as neighborhood guardians: A spatio-temporal analysis of community policing in Nairobi's suburbs
(Springer, 2017) Mburu, Lucy W.; Helbich, Marco
The efficacy of citizens to participate in neighborhood-watch activities and report signs of trouble is important for safeguarding communities against crime. Community policing is a key policing strategy for utilizing the capability of residents to solve local crime-related problems. However, variability in social cohesion among communities profoundly affects the contribution of individuals towards policing. After 7 years of a community policing intervention in suburban Nairobi, Kenya, this study assesses the program as a state-initiated and community-sustained security venture. We compare micro-scaled concentrations of different property and violent crimes to identify geographic variations over time using kernel density estimates and spatio-temporal scan statistics. Multi-level regression models assess the direct and conditioned perceptions of individuals and their neighbors, and how these perceptions influenced crime variation during the pre- and post-intervention periods of community policing. Both the density estimates and the scan statistics pinpoint a disproportionate crime reduction across neighborhoods. The research findings also depict an interaction between the communal willingness to participate in neighborhood-watch activities and the relative crime decline. In particular, those communities that have good relations with the police are more inclined to involve themselves in community policing. The findings of this study are discussed in terms of their implications for policy.
Crime risk estimation with a commuter- harmonized ambient population
(Taylor & Francis Group, 2016) Mburu, Lucy W.; Helbich, Marco
Residential population data are frequently employed to link the crime incidence of an area with the number of residents to estimate the underlying risk. Human mobility patterns cause shifts in the baseline population, however, that can potentially influence the crime statistics. This study therefore employed an ambient population that combined residential population data with data depicting the commuting activity in small administrative areas. The effects of the commuter-harmonized ambient population on crime were then evaluated in a series of negative binomial regression models. The models also controlled for criminogenic factors and incorporated eigenvector spatial filtering to adjust for spatial effects. The results show significant effects of commuting patterns on crime outcomes. For certain crimes, such as violence, theft, and disorder, the inbound commuters are significantly associated with high risk. It was further discovered that an offset variable comprising the commuter-harmonized ambient population data models the crime outcomes more reliably than when residential population data are used. Spatial filtering was found to effectively eradicate residual spatial autocorrelation after accounting for effects of the predictor variables. We conclude that calculating crime rates using the residential population does not constitute an accurate risk measure and that the ambient population has crucial implications for realistic and reliable target representation and crime modeling.
Environmental risk factors influencing bicycle theft:
(PubMed Central, 2016) Mburu, Lucy W.; Helbich, Marco
Abstract Urban authorities are continuously drawing up policies to promote cycling among commuters. However, these initiatives are counterproductive for the targeted objectives because they increase opportunities for bicycle theft. This paper explores Inner London as a case study to address place-specific risk factors for bicycle theft at the street-segment level while controlling for seasonal variation. The presence of certain public amenities (e.g., bicycle stands, railway stations, pawnshops) was evaluated against locations of bicycle theft between 2013 and 2016 and risk effects were estimated using negative binomial regression models. Results showed that a greater level of risk stemmed from land-use facilities than from area-based socioeconomic status. The presence of facilities such as train stations, vacant houses, pawnbrokers and payday lenders increased bicycle theft, but no evidence was found that linked police stations with crime levels. The findings have significant implications for urban crime prevention with respect to non-residential land use.
Evaluating the accuracy and effectiveness of criminal geographic profiling methods: the case of Dandora, Kenya
(Taylor & Francis Group, 2014) Mburu, Lucy W.; Helbich, Marco
Criminal geographic profiling (CGP) prioritizes offender search, extensively reducing the resources expended in criminal investigations. The utility of CGP has, however, remained unclear when variations in environmental characteristics and offense type are introduced. This study evaluates several CGP strategies with data from Dandora, a small but densely populated suburb of Nairobi, Kenya. The research employs error distance and search-cost measures to determine CGP accuracy. Characterized by much shorter journeys to crime than those observed in Western cities, this study discovers significant applicability of CGP strategies in prioritizing offender searches. The negative exponential CGP strategy is identified to generate the most accurate geo-profiles.